Preparation of an amphiphilic BaTiO3 nanofluids and its application to oil displacement in low-permeability reservoirs
Bibliographic record
Abstract
The challenges of difficult injection and low recovery in low-permeability reservoirs significantly impact production efficiency. Nanofluid flooding presents a viable approach to improve to enhance recovery in such reservoirs. In this study, an amphiphilic J nano-BaTiO 3 particle (JNB-P) with a particle size of 50–90 nm was synthesized. Characterization results indicated that, compared to unmodified Nano BaTiO 3 particles, the JNB-P molecules introduced long-chain groups, leading to improved dispersion and a transition of the particle surface from hydrophilic to amphiphilic. Settling experiments identified the optimal dispersive nanofluid (JNB-FS) as a combination of 0.25 % PEG400 and 0.025 % JNB-P, with complete settling time for nanoparticles decreased as temperature or salinity increased. Emulsification experiments demonstrated that, at a water-to-oil volume ratio of 5:5, JNB-FS and crude oil successfully emulsified into an O/W emulsion with an average particle size of 0.791 μm. Interfacial tension experiments revealed that the interfacial tension of oil–water decreased by more than 50 times after adding JNB-FS. Furthermore, with increasing temperature, the degree of emulsification initially increased before decreasing, while emulsion stability, interfacial tension, and expansion modulus gradually decreased. As salinity increased, the degree of emulsification and emulsion stability declined, with interfacial tension first decreasing and then increasing, and the expansion modulus initially rising before falling. Wetting improvement experiments indicated that the core transitioned from lipophilicity to strong hydrophilicity after soaking in JNB-FS for 60 h. Finally, flow experiments demonstrated that compared to first water flooding, the oil recovery increased by 15.82 %. The oil displacement mechanism of JNB-FS primarily involved reducing interfacial tension, enhancing rock wettability, emulsification, and improving the permeability of the injected water via nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".